B.Sc. in Computer Science, Data Engineering, or a related field. 3+ years hands-on with large-scale data infrastructure. Strong Python (including Pandas). Deep SQL and NoSQL knowledge, with real performance-optimization experience. Proven experience with real-time/low-latency systems, and the data architecture and modeling instincts that make them fast. Comfort with big-data volumes, pipelines, and data lakes. Experience with streaming / event-driven design (e.g., Kafka). Spark / PySpark for large-scale processing, or a strong adjacent big-data background. Strong problem-solving skills and a proactive, independent mindset. Advantages Experience with ClickHouse. Data warehousing and lakehouse / open table formats (e.g., Apache Iceberg). Vector databases and embeddings - storing and serving them to support AI and data-science features (e.g., pgvector). Strong system design and data architecture sense - knows how to build the data model the right way. Experience using AI-assisted development tools (e.g., Cursor, Claude Code) in day-to-day work. Experience with security, fraud, or other high-volume telemetry data. Comfort with containerized infrastructure (Docker, basic Kubernetes) and light DevOps (CI/CD, Git). ElasticSearch, Redis, DynamoDB, or Metabase. Who you are Independent and self-driven - comfortable owning systems end to end. Growth-oriented - eager to develop and take on more over time. Collaborative - communicates well across engineering, security, and data science. Adaptable - thrives in a fast-paced environment with a can-do attitude.
